Milestone 4 Group Z

Web access to knitted HTML output

We have decided to take a leap and try plot_ly and knit to html output for this milestone. The html page is hosted as Github Pages and is accessible in this address: https://tin6150.github.io/phw251_group_z/milestone4_groupZ.html

Project and milestone overview

{Pasted to group’s gdoc}

R setup and data preparation

(This code block omitted for brevity, please refer to source at our github repo )

Visualizations & Code

Data needed to make decision: -which counties share all 3 attributes: low pop(rural), high median age, & high proportion of renters vs homeowners -which 5 counties have highest mortality rates due to chronic illness -what counties have received little to no funding recently? —not quite sure about this one

Rural Counties Deserving More HCAI Funding

Table 1 shows that the 5 counties of Siskiyou, Inyo, Mariposa, Plumas and Modoc have low population density (they qualify as Rural per National Rural Development Partnership’s definition), have a high median age, and fairly high ratio of renters. More importantly, they have one of the highest percentage of chronic diseases, and no HCAI fundings that are “In Closure” as of 2022-08-11.

Note that the latest population data is from 2012.

viz_focus = viz_fund_dem_chron %>%
  filter( rural_class      == "rural",
          high_med_age     == TRUE 
          ##high_rental    == TRUE  
          )

focus_table = viz_focus %>%
  select( County, 
          pop12_sqmi,
          #rural_class,  
          #low_pop,
          med_age,
          #high_med_age,
          rent_own_ratio,
          #high_rental,
          pct,                    # prevalence,         
          Numeric_Cost, 
          #fund_per_cap,
          #`Number of OSHPD Projects`,
  ) %>% 
  arrange( desc( pct )) %>%
  rename(
    `Pop Density` = pop12_sqmi,
    `Median Age` = med_age,
    `Rent:Own Ratio` = rent_own_ratio,
    `% Chronic`      = pct,
    `HCAI Fund in 2022` = Numeric_Cost
  ) %>%  
  head( 5 ) 

kable( focus_table,
       booktabs=T,
       digits=c(0,1,1,2,2,0),
       format.args=list(big.mark=','),
       caption = "Rural Counties with high median age, rental ratio, and chronic disease rate",
       )

Rural Counties with high median age, rental ratio, and chronic disease rate

County	Pop Density	Median Age	Rent:Own Ratio	% Chronic	HCAI Fund in 2022
Siskiyou	7.1	46.8	0.54	2.86	0
Inyo	1.8	45.5	0.57	1.88	0
Mariposa	12.6	49.2	0.47	1.72	0
Plumas	7.7	49.5	0.44	1.69	0
Modoc	2.3	46.0	0.46	1.43	0

Print Quality Plot or Chart

The following boxplot summarizes chronic disease mortality rates from all CA counties, grouped according to HCAI funding amounts for in closure projects as of August 2022. The funding amounts were categorized as “high” if they were above the mean amount, low if they were below the mean, and “no funding” if no funding for in closure projects was reported.

(Audrey, so what is graph telling the audience?)

funding_chronic <- funding_data %>% 
  filter( `OSHPD Project Status` == "In Closure") %>%
  filter( `Data Generation Date` == as_date( "2022-08-11")) %>%
  mutate(funding_amount = case_when(
    Numeric_Cost > 12239849 ~ "High Funding", 
    Numeric_Cost == 0 ~ "No Funding", 
    Numeric_Cost < 12239849 ~ "Low Funding"
  )) %>%
  inner_join(demographics_chronic, funding_data_all_counties, by = "County") %>%
  select(pct, County, funding_amount, rural_class, Numeric_Cost)

plot_ly(
  funding_chronic,
  y=~pct,
  color= ~funding_amount,
  type="box"
) %>%
   layout(
    title="Chronic Disease Mortality Rates & HCAI Funding",
    yaxis=list(title="Chronic Disease Rate"))

Funding for select counties

counties_funding_comp = funding_data %>%
  filter( `OSHPD Project Status` == "In Closure"  ) %>%
  filter( `Data Generation Date` ==
            as_date( "2022-08-11" )
            #as_date( "2020-01-01" )
            #as_date( "2016-01-01" )
          ) %>%
  arrange( Numeric_Cost )

#fig2 = plot_ly( data = funding_data_1county ) %>%
#fig2 = plot_ly( data = funding_data_selected_counties ) %>%
fig2 = plot_ly( data = counties_funding_comp ) %>%
  add_trace(x = ~`County`,
            y = ~Numeric_Cost,
            type = 'bar',
            name = 'Funding across 58 counties',
            marker = list(color = 'rgb(187, 216, 228)'),
            hoverinfo = "text",
            text = ~paste(round(Numeric_Cost, 0), ' US$')  ) %>%
   layout(
            title="Funding across 58 counties"
         )

fig2

Mortality Rate by County

Below is a graph of Mortality Rate for Chronic diseases (as defined by CDC) across 11 rural counties (as defined by National Rural Development Partnership) The 5 counties of focus have the highest mortality rates in this group.

Note that we don’t have disease data for Alpine or Sierra county.

#chronic_focus_counties = rural_counties %>% 
chronic_focus_counties = inner_join(
    demographics_chronic,
    rural_counties,
    by = "County"                   ) %>%
  mutate( ctyColor = case_when(
    County == "Siskiyou" ~ "red", 
    County == "Inyo"     ~ "darkorange" , 
    County == "Mariposa" ~ "blue", 
    County == "Plumas"   ~ "darkblue",
    County == "Modoc"    ~ "darkcyan",
    TRUE                 ~ "rgb(187, 216, 228)"
  ) )


fig3 = plot_ly( data = chronic_focus_counties ,
            text = ~paste(County, round(pct, 1), "%"     )  # add_trace overwrite this :-\
            ) %>%
    add_trace(x = ~County,
            y = ~pct,
            name = 'Chronic Mortality Rates by Counties',
            marker = list(color = ~ctyColor),
            hoverinfo = "text",
            text = ~paste(round(pct, 1), "%" ),  
            type = 'bar') %>%
   layout(
            title = "Chronic Mortality Rates For California's Rural Counties", 
            yaxis = list( title="% Mortality Rate" ),
            xaxis = list( title="County")
         )

fig3

## TBD: find a way to arrange them by mortality rate

Table showing most common disease by rural county* thinking of ideas, will have it done by 1pm tomorrow before work!